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Define Fixed Goals Instead of Open Instructions
Instead of giving one-off instructions, define a final state and a set of recurring commands. Tools like Claude Code use commands to check for new inputs or force an agent to work until specific criteria are met. This shifts your role from active pilot to system architect.
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Build Deterministic Harnesses
Purely autonomous agents often drift or fail during long tasks. Build a deterministic harness that forces the AI through specific stages like research, implementation, and testing. By making the process structure rigid and the reasoning fluid, you ensure the agent stays on track.
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Use Mixed Model Orchestration
Running flagship models for every step of a loop is prohibitively expensive. Use smaller, cheaper models for routine tasks like classification or planning. Save high-end models specifically for complex coding or final implementation to keep token costs manageable.
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Maintain State Outside the Model
Long-running agent sessions eventually fail as the context window fills with irrelevant history. Move the workflow state to an external database. This allows the system to remain durable and resumable even if a specific session crashes or reaches its memory limit.
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Execute Tasks in Parallel Isolation
To handle multiple tasks simultaneously, run parallel agents in isolated environments. Each agent should have its own branch and database instance to avoid conflicts. This structure allows a single orchestrator to manage multiple workers without them overwriting each other's work.
Why it matters
Small businesses often lack the headcount to manage every individual AI interaction. Moving to structured loops allows you to automate entire workflows rather than just individual tasks. These techniques turn AI from a chat interface into a background system that actually finishes projects.